def make_dataset(field,data,field_attrs,file_obj,group_obj,scale_dict,dimScale=False):
dimensions = field_attrs[field]['dimensions'].split(',')
if field_attrs[field]['datatype'].startswith('int'):
data = np.nan_to_num(data,nan=np.iinfo(np.dtype(field_attrs[field]['datatype'])).max)
fillvalue = np.iinfo(np.dtype(field_attrs[field]['datatype'])).max
elif field_attrs[field]['datatype'].startswith('float'):
data = np.nan_to_num(data,nan=np.finfo(np.dtype(field_attrs[field]['datatype'])).max)
fillvalue = np.finfo(np.dtype(field_attrs[field]['datatype'])).max
dset = group_obj.create_dataset(field.encode('ASCII'),data=data,fillvalue=fillvalue,chunks=True,compression=6,dtype=field_attrs[field]['datatype'])
for ii,dim in enumerate(dimensions):
dset.dims[ii].label = scale[dim.strip()]
if dimScale:
dset.make_scale(field)
else:
if dim.strip().startswith('Nt'):
dset.dims[ii].attach_scale(file_obj[scale[dim.strip()]])
else:
dset.dims[ii].attach_scale(group_obj[scale[dim.strip()]])
for attr in attr_names:
if 'dimensions' not in attr and 'datatype' not in attr:
create_attribute(dset.id, attr, [], str(field_attrs[field][attr]))
if field_attrs[field]['datatype'].startswith('int'):
dset.attrs['_FillValue'.encode('ASCII')] = np.iinfo(np.dtype(field_attrs[field]['datatype'])).max
elif field_attrs[field]['datatype'].startswith('float'):
dset.attrs['_FillValue'.encode('ASCII')] = np.finfo(np.dtype(field_attrs[field]['datatype'])).max
return file_obj
def ATL14_write():
dz_dict = {
'x': 'x', # ATL14 varname : z0.h5 varname
'y': 'y',
'h': 'z0',
'h_sigma': 'sigma_z0',
# 'cell_area':'area',
# 'data_count':'count',
'misfit_rms': 'misfit_rms',
'misfit_scaled_rms': 'misfit_scaled_rms',
}
scale = {
'Nx': 'x',
'Ny': 'y',
}
# establish output file
fileout = 'ATL14_tile01.h5'
print('output file', fileout)
if os.path.isfile(fileout):
os.remove(fileout)
with h5py.File(fileout.encode('ASCII'), 'w') as fo:
# get handle for input file with ROOT and height_change variables.
FH = h5py.File('z0.h5', 'r')
if 'z0' not in FH:
print('no zo')
FH.close()
exit(-1)
with open('ATL14_output_attrs.csv', 'r',
encoding='utf-8-sig') as attrfile:
reader = list(csv.DictReader(attrfile))
# group_names = set([row['group'] for row in reader])
attr_names = [
x for x in reader[0].keys() if x != 'field' and x != 'group'
]
# work ROOT group first
field_names = [
row['field'] for row in reader if 'ROOT' in row['group']
]
print(field_names)
#establish variables that are dimension scales first
for field in ['x', 'y']:
print('field', field)
field_attrs = {
row['field']: {
attr_names[ii]: row[attr_names[ii]]
for ii in range(len(attr_names))
}
for row in reader if field in row['field']
}
dimensions = field_attrs[field]['dimensions'].split(',')
data = np.array(FH['z0'][dz_dict[field]])
data = np.nan_to_num(
data,
nan=np.finfo(np.dtype(field_attrs[field]['datatype'])).max)
fillvalue = np.finfo(np.dtype(field_attrs[field]['datatype'])).max
dset = fo.create_dataset(field.encode('ASCII'),
data=data,
fillvalue=fillvalue,
chunks=True,
compression=6,
dtype=field_attrs[field]['datatype'])
dset.make_scale(field)
for ii, dim in enumerate(dimensions):
print(field, dim)
dset.dims[ii].label = scale[dim.strip()]
for attr in attr_names:
if 'dimensions' not in attr and 'datatype' not in attr:
create_attribute(dset.id, attr, [],
str(field_attrs[field][attr]))
if field_attrs[field]['datatype'].startswith('int'):
dset.attrs['_FillValue'.encode('ASCII')] = np.iinfo(
np.dtype(field_attrs[field]['datatype'])).max
elif field_attrs[field]['datatype'].startswith('float'):
dset.attrs['_FillValue'.encode('ASCII')] = np.finfo(
np.dtype(field_attrs[field]['datatype'])).max
for field in [
item for item in field_names if item != 'x' and item != 'y'
]:
# read attrs from .csv
field_attrs = {
row['field']: {
attr_names[ii]: row[attr_names[ii]]
for ii in range(len(attr_names))
}
for row in reader if field in row['field']
}
dimensions = field_attrs[field]['dimensions'].split(',')
if dz_dict.get(field) != None: # if key in dz_dict
data = np.squeeze(np.array(FH['z0'][dz_dict[field]]))
else:
data = np.ndarray(shape=tuple(
[ii + 1 for ii in range(len(dimensions))]),
dtype=float)
print('line 93', field, dimensions)
data = np.nan_to_num(
data,
nan=np.finfo(np.dtype(field_attrs[field]['datatype'])).max)
print('shape', data.shape)
fillvalue = np.finfo(np.dtype(field_attrs[field]['datatype'])).max
dset = fo.create_dataset(field.encode('ASCII'),
data=data,
fillvalue=fillvalue,
chunks=True,
compression=6,
dtype=field_attrs[field]['datatype'])
for ii, dim in enumerate(dimensions):
print('line 98', ii, dim)
dset.dims[ii].label = scale[dim.strip()]
dset.dims[ii].attach_scale(fo[scale[dim.strip()]])
for attr in attr_names:
if 'dimensions' not in attr and 'datatype' not in attr:
create_attribute(dset.id, attr, [],
str(field_attrs[field][attr]))
if field_attrs[field]['datatype'].startswith('int'):
dset.attrs['_FillValue'.encode('ASCII')] = np.iinfo(
np.dtype(field_attrs[field]['datatype'])).max
elif field_attrs[field]['datatype'].startswith('float'):
dset.attrs['_FillValue'.encode('ASCII')] = np.finfo(
np.dtype(field_attrs[field]['datatype'])).max
FH.close()
#
return fileout
def write_to_file(self, fileout, params_11=None):
# Generic code to write data from an ATL11 object to an h5 file
# Input:
# fileout: filename of hdf5 filename to write
# Optional input:
# parms_11: ATL11.defaults structure
beam_pair_name='/pt%d' % self.beam_pair
beam_pair_name=beam_pair_name.encode('ASCII')
if os.path.isfile(fileout):
f = h5py.File(fileout.encode('ASCII'),'r+')
if beam_pair_name in f:
del f[beam_pair_name]
else:
f = h5py.File(fileout.encode('ASCII'),'w')
g=f.create_group(beam_pair_name)
# set the output pair and track attributes
g.attrs['beam_pair'.encode('ASCII')]=self.beam_pair
g.attrs['ReferenceGroundTrack'.encode('ASCII')]=self.track_num
g.attrs['first_cycle'.encode('ASCII')]=self.cycles[0]
g.attrs['last_cycle'.encode('ASCII')]=self.cycles[1]
# put default parameters as top level attributes
if params_11 is None:
params_11=ATL11.defaults()
# write each variable in params_11 as an attribute
for param, val in vars(params_11).items():
if not isinstance(val,(dict,type(None))):
try:
if param == 'ATL06_xover_field_list':
xover_attr=getattr(params_11, param)
xover_attr = [x.encode('ASCII') for x in xover_attr]
g.attrs[param.encode('ASCII')]=xover_attr
else:
g.attrs[param.encode('ASCII')]=getattr(params_11, param)
except Exception as e:
print("write_to_file:could not automatically set parameter: %s error = %s" % (param,str(e)))
continue
# put groups, fields and associated attributes from .csv file
with importlib.resources.path('ATL11','package_data') as pp:
with open(os.path.join(pp,'ATL11_output_attrs.csv'),'r') as attrfile:
reader=list(csv.DictReader(attrfile))
group_names=set([row['group'] for row in reader])
attr_names=[x for x in reader[0].keys() if x != 'field' and x != 'group']
# start with 'ROOT' group
list_vars=getattr(self,'ROOT').list_of_fields
list_vars.append('cycle_number')
# establish the two main dimension scales
for field in ['ref_pt','cycle_number']:
field_attrs = {row['field']: {attr_names[ii]:row[attr_names[ii]] for ii in range(len(attr_names))} for row in reader if 'ROOT' in row['group']}
dimensions = field_attrs[field]['dimensions'].split(',')
data = getattr(getattr(self,'ROOT'),field)
dset = g.create_dataset(field.encode('ASCII'),data=data,chunks=True,compression=6,dtype=field_attrs[field]['datatype'].lower()) #,fillvalue=fillvalue)
dset.dims[0].label = field
for attr in attr_names:
if 'dimensions' not in attr and 'datatype' not in attr:
create_attribute(dset.id, attr, [], str(field_attrs[field][attr]))
if field_attrs[field]['datatype'].startswith('int'):
dset.attrs['_FillValue'.encode('ASCII')] = np.iinfo(np.dtype(field_attrs[field]['datatype'].lower())).max
elif field_attrs[field]['datatype'].startswith('Float'):
dset.attrs['_FillValue'.encode('ASCII')] = np.finfo(np.dtype(field_attrs[field]['datatype'].lower())).max
for field in [item for item in list_vars if (item != 'ref_pt') and (item != 'cycle_number')]:
field_attrs = {row['field']: {attr_names[ii]:row[attr_names[ii]] for ii in range(len(attr_names))} for row in reader if 'ROOT' in row['group']}
dimensions = field_attrs[field]['dimensions'].split(',')
data = getattr(getattr(self,'ROOT'),field)
# change nans to proper invalid, depending on datatype
if field_attrs[field]['datatype'].startswith('int'):
data = np.nan_to_num(data,nan=np.iinfo(np.dtype(field_attrs[field]['datatype'].lower())).max)
data = data.astype('int') # don't change to int before substituting nans with invalid.
fillvalue = np.iinfo(np.dtype(field_attrs[field]['datatype'].lower())).max
elif field_attrs[field]['datatype'].startswith('Float'):
data = np.nan_to_num(data,nan=np.finfo(np.dtype(field_attrs[field]['datatype'].lower())).max)
fillvalue = np.finfo(np.dtype(field_attrs[field]['datatype'].lower())).max
dset = g.create_dataset(field.encode('ASCII'),
data=data,fillvalue=fillvalue,
chunks=True,
compression=6,
dtype=field_attrs[field]['datatype'].lower())
dset.dims[0].label = field
for ii,dim in enumerate(dimensions):
dim=dim.strip()
if 'N_pts' in dim:
dset.dims[ii].attach_scale(g['ref_pt'])
dset.dims[ii].label = 'ref_pt'
if 'N_cycles' in dim:
dset.dims[ii].attach_scale(g['cycle_number'])
dset.dims[ii].label = 'cycle_number'
for attr in attr_names:
if 'dimensions' not in attr and 'datatype' not in attr:
create_attribute(dset.id, attr, [], str(field_attrs[field][attr]))
if field_attrs[field]['datatype'].startswith('int'):
dset.attrs['_FillValue'.encode('ASCII')] = np.iinfo(np.dtype(field_attrs[field]['datatype'].lower())).max
elif field_attrs[field]['datatype'].startswith('Float'):
dset.attrs['_FillValue'.encode('ASCII')] = np.finfo(np.dtype(field_attrs[field]['datatype'].lower())).max
for group in [item for item in group_names if item != 'ROOT']:
if hasattr(getattr(self,group),'list_of_fields'):
grp = g.create_group(group.encode('ASCII'))
field_attrs = {row['field']: {attr_names[ii]:row[attr_names[ii]] for ii in range(len(attr_names))} for row in reader if group in row['group']}
if 'crossing_track_data' in group:
this_ref_pt=getattr(getattr(self,group),'ref_pt')
if len(this_ref_pt) > 0:
dset = grp.create_dataset('ref_pt'.encode('ASCII'),data=this_ref_pt,chunks=True,compression=6,dtype=field_attrs['ref_pt']['datatype'])
else:
dset = grp.create_dataset('ref_pt'.encode('ASCII'), shape=[0],chunks=True,compression=6,dtype=np.int32)
dset.dims[0].label = 'ref_pt'.encode('ASCII')
for attr in attr_names:
if 'dimensions' not in attr and 'datatype' not in attr:
create_attribute(dset.id, attr, [], field_attrs['ref_pt'][attr])
if 'ref_surf' in group:
dset = grp.create_dataset('poly_exponent_x'.encode('ASCII'),data=np.array([item[0] for item in params_11.poly_exponent_list]),chunks=True,compression=6,dtype=field_attrs['poly_exponent_x']['datatype'])
dset.dims[0].label = 'poly_exponent_x'.encode('ASCII')
for attr in attr_names:
if 'dimensions' not in attr and 'datatype' not in attr:
create_attribute(dset.id, attr, [], field_attrs['poly_exponent_x'][attr])
dset = grp.create_dataset('poly_exponent_y'.encode('ASCII'),data=np.array([item[1] for item in params_11.poly_exponent_list]),chunks=True,compression=6, dtype=field_attrs['poly_exponent_y']['datatype'])
dset.dims[0].label = 'poly_exponent_y'.encode('ASCII')
for attr in attr_names:
if 'dimensions' not in attr and 'datatype' not in attr:
create_attribute(dset.id, attr, [], field_attrs['poly_exponent_y'][attr])
grp.attrs['poly_exponent_x'.encode('ASCII')]=np.array([item[0] for item in params_11.poly_exponent_list], dtype=int)
grp.attrs['poly_exponent_y'.encode('ASCII')]=np.array([item[1] for item in params_11.poly_exponent_list], dtype=int)
grp.attrs['slope_change_t0'.encode('ASCII')]=np.mean(self.slope_change_t0).astype('int')
g.attrs['N_poly_coeffs'.encode('ASCII')]=int(self.N_coeffs)
list_vars=getattr(self,group).list_of_fields
if group == 'crossing_track_data':
list_vars.remove('ref_pt') # handled above
if list_vars is not None:
for field in list_vars:
dimensions = field_attrs[field]['dimensions'].split(',')
data = getattr(getattr(self,group),field)
# change nans to proper invalid, depending on datatype
if field_attrs[field]['datatype'].startswith('int'):
data = np.nan_to_num(data,nan=np.iinfo(np.dtype(field_attrs[field]['datatype'])).max)
data = data.astype('int') # don't change to int before substituting nans with invalid.
fillvalue = np.iinfo(np.dtype(field_attrs[field]['datatype'].lower())).max
elif field_attrs[field]['datatype'].startswith('Float'):
data = np.nan_to_num(data,nan=np.finfo(np.dtype(field_attrs[field]['datatype'].lower())).max)
fillvalue = np.finfo(np.dtype(field_attrs[field]['datatype'].lower())).max
dset = grp.create_dataset(field.encode('ASCII'),data=data,fillvalue=fillvalue,chunks=True,compression=6,dtype=field_attrs[field]['datatype'].lower())
for ii,dim in enumerate(dimensions):
dim=dim.strip()
if 'N_pts' in dim:
dset.dims[ii].attach_scale(g['ref_pt'])
dset.dims[ii].label = 'ref_pt'
if 'N_cycles' in dim:
dset.dims[ii].attach_scale(g['cycle_number'])
dset.dims[ii].label = 'cycle_number'
if 'N_coeffs' in dim:
dset.dims[ii].attach_scale(grp['poly_exponent_x'])
dset.dims[ii].attach_scale(grp['poly_exponent_y'])
dset.dims[ii].label = '(poly_exponent_x, poly_exponent_y)'
if 'Nxo' in dim:
dset.dims[ii].attach_scale(grp['ref_pt'])
dset.dims[ii].label = 'ref_pt'
for attr in attr_names:
if 'dimensions' not in attr and 'datatype' not in attr:
create_attribute(dset.id, attr, [], str(field_attrs[field][attr]))
if field_attrs[field]['datatype'].startswith('int'):
dset.attrs['_FillValue'.encode('ASCII')] = np.iinfo(np.dtype(field_attrs[field]['datatype'].lower())).max
elif field_attrs[field]['datatype'].startswith('Float'):
dset.attrs['_FillValue'.encode('ASCII')] = np.finfo(np.dtype(field_attrs[field]['datatype'].lower())).max
f.close()
return
def poly_buffered_linestring(outfile):
lonlat_11 = []
with h5py.File(outfile, 'r') as h5f:
for pair in ['pt1', 'pt2', 'pt3']:
try:
lonlat_11 += [
np.c_[h5f[pair + '/longitude'], h5f[pair + '/latitude']]
]
except Exception as e:
print(e)
print('avg lat lonlat_11[0]',
np.sum(lonlat_11[0][:, 1]) / len(lonlat_11[0]))
if np.sum(lonlat_11[0][:, 1]) / len(lonlat_11[0]) >= 0.0:
polarEPSG = 3413
else:
polarEPSG = 3031
xformer_ll2pol = pyproj.Transformer.from_crs(4326, polarEPSG)
xformer_pol2ll = pyproj.Transformer.from_crs(polarEPSG, 4326)
xy_11 = []
for ll in lonlat_11:
xy_11 += [np.c_[xformer_ll2pol.transform(ll[:, 1], ll[:, 0])]]
lines = []
for xx in xy_11:
lines += [shapely.geometry.LineString(xx)]
line_simp = []
for line in lines:
line_simp += [line.simplify(tolerance=100)]
all_lines = shapely.geometry.MultiLineString(line_simp)
common_buffer = all_lines.buffer(3000, 4)
common_buffer = common_buffer.simplify(tolerance=500)
xpol, ypol = np.array(common_buffer.exterior.coords.xy)
y1, x1 = xformer_pol2ll.transform(xpol, ypol)
print("polygon size:", len(x1))
with h5py.File(outfile, 'r+') as h5f:
if '/orbit_info/bounding_polygon_dim1' in h5f:
del h5f['/orbit_info/bounding_polygon_dim1']
del h5f['/orbit_info/bounding_polygon_lon1']
del h5f['/orbit_info/bounding_polygon_lat1']
if '/orbit_info/bounding_polygon_dim2' in h5f:
del h5f['/orbit_info/bounding_polygon_dim2']
del h5f['/orbit_info/bounding_polygon_lon2']
del h5f['/orbit_info/bounding_polygon_lat2']
h5f.create_dataset('/orbit_info/bounding_polygon_dim1',
data=np.arange(1,
np.size(x1) + 1),
chunks=True,
compression=6,
dtype='int32')
create_attribute(h5f['orbit_info/bounding_polygon_dim1'].id,
'description', [], 'Polygon extent vertex count')
create_attribute(h5f['orbit_info/bounding_polygon_dim1'].id, 'units',
[], '1')
create_attribute(h5f['orbit_info/bounding_polygon_dim1'].id,
'long_name', [], 'Polygon vertex count')
create_attribute(h5f['orbit_info/bounding_polygon_dim1'].id, 'source',
[], 'model')
dset = h5f.create_dataset('/orbit_info/bounding_polygon_lon1',
data=x1,
chunks=True,
compression=6,
dtype='float32')
dset.dims[0].attach_scale(h5f['orbit_info']['bounding_polygon_dim1'])
create_attribute(h5f['orbit_info/bounding_polygon_lon1'].id,
'description', [], 'Polygon extent vertex longitude')
create_attribute(h5f['orbit_info/bounding_polygon_lon1'].id, 'units',
[], 'degrees East')
create_attribute(h5f['orbit_info/bounding_polygon_lon1'].id,
'long_name', [], 'Polygon vertex longitude')
create_attribute(h5f['orbit_info/bounding_polygon_lon1'].id, 'source',
[], 'model')
create_attribute(h5f['orbit_info/bounding_polygon_lon1'].id,
'coordinates', [], 'bounding_polygon_dim1')
dset = h5f.create_dataset('/orbit_info/bounding_polygon_lat1',
data=y1,
chunks=True,
compression=6,
dtype='float32')
dset.dims[0].attach_scale(h5f['orbit_info']['bounding_polygon_dim1'])
create_attribute(h5f['orbit_info/bounding_polygon_lat1'].id,
'description', [], 'Polygon extent vertex latitude')
create_attribute(h5f['orbit_info/bounding_polygon_lat1'].id, 'units',
[], 'degrees North')
create_attribute(h5f['orbit_info/bounding_polygon_lat1'].id,
'long_name', [], 'Polygon vertex latitude')
create_attribute(h5f['orbit_info/bounding_polygon_lat1'].id, 'source',
[], 'model')
create_attribute(h5f['orbit_info/bounding_polygon_lat1'].id,
'coordinates', [], 'bounding_polygon_dim1')
def write_METADATA(outfile, infiles):
if os.path.isfile(outfile):
#
# Call filemeta, copies METADATA group from template
#
filemeta(outfile, infiles)
g = h5py.File(outfile, 'r+')
gf = g.create_group('METADATA/Lineage/ATL06'.encode(
'ASCII', 'replace'))
fname = []
sname = []
scycle = []
ecycle = []
srgt = []
ergt = []
sregion = []
eregion = []
sgeoseg = []
egeoseg = []
sorbit = []
eorbit = []
uuid = []
version = []
for ii, infile in enumerate(sorted(infiles)):
fname.append(os.path.basename(infile).encode('ASCII'))
if os.path.isfile(infile):
f = h5py.File(infile, 'r')
# Read the datasets from ATL06 ancillary_data, where available
# All fields must be arrays, not just min/max, even if just repeats
#
sname.append(f['/'].attrs['short_name'])
uuid.append(f['/'].attrs['identifier_file_uuid'])
scycle.append(f['ancillary_data/start_cycle'])
ecycle.append(f['ancillary_data/end_cycle'])
sorbit.append(f['ancillary_data/start_orbit'])
eorbit.append(f['ancillary_data/end_orbit'])
sregion.append(f['ancillary_data/start_region'])
eregion.append(f['ancillary_data/end_region'])
srgt.append(f['ancillary_data/start_rgt'])
ergt.append(f['ancillary_data/end_rgt'])
version.append(f['ancillary_data/version'])
for pt in g.keys():
if pt.startswith('pt'):
sgeoseg = np.min([sgeoseg, np.min(g[pt]['ref_pt'][:])])
egeoseg = np.max([egeoseg, np.max(g[pt]['ref_pt'][:])])
gf.attrs['description'] = 'ICESat-2 ATLAS Land Ice'.encode(
'ASCII', 'replace')
#
# Use create_attribute for strings to get ASCII and NULLTERM
#
create_attribute(gf.id, 'fileName', [2], fname)
create_attribute(gf.id, 'shortName', [2], sname)
gf.attrs['start_orbit'] = np.ravel(sorbit)
gf.attrs['end_orbit'] = np.ravel(eorbit)
gf.attrs['start_cycle'] = np.ravel(scycle)
gf.attrs['end_cycle'] = np.ravel(ecycle)
gf.attrs['start_rgt'] = np.ravel(srgt)
gf.attrs['end_rgt'] = np.ravel(ergt)
gf.attrs['start_region'] = np.ravel(sregion)
gf.attrs['end_region'] = np.ravel(eregion)
gf.attrs['start_geoseg'] = np.repeat(sgeoseg, np.size(sregion))
gf.attrs['end_geoseg'] = np.repeat(egeoseg, np.size(sregion))
create_attribute(gf.id, 'uuid', [2], uuid)
gf.attrs['version'] = np.ravel(version)
g.close()
return outfile
def filemeta(outfile, infiles):
orbit_info={'crossing_time':0., 'cycle_number':0, 'lan':0., \
'orbit_number':0., 'rgt':0, 'sc_orient':0, 'sc_orient_time':0.}
root_info={'date_created':'', 'geospatial_lat_max':0., 'geospatial_lat_min':0., \
'geospatial_lat_units':'', \
'geospatial_lon_max':0., 'geospatial_lon_min':0., 'geospatial_lon_units':'', \
'hdfversion':'', 'history':'', \
'identifier_file_uuid':'', 'identifier_product_format_version':'', 'time_coverage_duration':0., \
'time_coverage_end':'', 'time_coverage_start':''}
# copy METADATA group from ATL11 template. Make lineage/cycle_array conatining each ATL06 file, where the ATL06 filenames
if os.path.isfile(outfile):
g = h5py.File(outfile, 'r+')
for ii, infile in enumerate(sorted(infiles)):
m = h5py.File(
os.path.dirname(os.path.realpath(__file__)) +
'/atl11_metadata_template.h5', 'r')
if ii == 0:
if 'METADATA' in list(g['/'].keys()):
del g['METADATA']
# get all METADATA groups except Lineage, which we set to zero
m.copy('METADATA', g)
# fix up Lineage
if 'Lineage' in list(g['METADATA'].keys()):
del g['METADATA']['Lineage']
g['METADATA'].create_group('Lineage'.encode(
'ASCII', 'replace'))
gf = g['METADATA']['Lineage'].create_group('ANC36-11'.encode(
'ASCII', 'replace'))
gf = g['METADATA']['Lineage'].create_group('ANC38-11'.encode(
'ASCII', 'replace'))
gf = g['METADATA']['Lineage'].create_group('Control'.encode(
'ASCII', 'replace'))
# Add in needed root attributes
create_attribute(
gf.id, 'description', [],
'Exact command line execution of ICESat-2/ATL11 algorithm providing all of the conditions required for each individual run of the software.'
)
create_attribute(gf.id, 'shortName', [], 'CNTL')
create_attribute(gf.id, 'version', [], '1')
create_attribute(gf.id, 'control', [], ' '.join(sys.argv))
# handle METADATA aatributes
create_attribute(g['METADATA/DatasetIdentification'].id,
'fileName', [], os.path.basename(outfile))
create_attribute(g['METADATA/DatasetIdentification'].id,
'uuid', [], str(uuid.uuid4()))
create_attribute(g['METADATA/ProcessStep/PGE'].id,
'runTimeParameters', [], ' '.join(sys.argv))
create_attribute(g['METADATA/ProcessStep/PGE'].id,
'identifier', [], identifier())
create_attribute(g['METADATA/ProcessStep/PGE'].id,
'softwareDate', [], softwareDate())
create_attribute(g['METADATA/ProcessStep/PGE'].id,
'softwareTitle', [], softwareTitle())
gf = g.create_group('quality_assessment'.encode(
'ASCII', 'replace'))
if os.path.isfile(infile):
f = h5py.File(infile, 'r')
f.copy('quality_assessment/qa_granule_fail_reason',
g['quality_assessment'])
f.copy('quality_assessment/qa_granule_pass_fail',
g['quality_assessment'])
f.copy('ancillary_data', g)
del g['ancillary_data/land_ice']
gf = g['METADATA']['Lineage']['Control'].attrs[
'control'].decode()
g['ancillary_data/control'][...] = gf.encode(
'ASCII', 'replace')
del g['METADATA/Extent']
f.copy('METADATA/Extent', g['METADATA'])
start_delta_time = f['ancillary_data/start_delta_time'][0]
create_attribute(g.id, 'short_name', [], 'ATL11')
for key, keyval in root_info.items():
dsname = key
if key == 'date_created' or key == 'history':
val = str(datetime.now().date())
val = val + 'T' + str(datetime.now().time()) + 'Z'
create_attribute(g.id, key, [], val)
create_attribute(g['METADATA/ProcessStep/PGE'].id,
'stepDateTime', [], val)
create_attribute(
g['METADATA/DatasetIdentification'].id,
'creationDate', [], val)
continue
if key == 'identifier_product_format_version':
val = softwareVersion()
create_attribute(g.id, key, [], val)
create_attribute(g['METADATA/ProcessStep/PGE'].id,
'softwareVersion', [], val)
create_attribute(
g['METADATA/DatasetIdentification'].id,
'VersionID', [], val)
create_attribute(
g['METADATA/SeriesIdentification'].id,
'VersionID', [], series_version())
continue
if key == 'time_coverage_start':
val = f.attrs[key].decode()
create_attribute(g.id, key, [], val)
continue
if key == 'time_coverage_end' or key == 'time_coverage_duration':
continue
if dsname in f.attrs:
if isinstance(keyval, float):
val = f.attrs[key]
g.attrs[key] = val
else:
val = f.attrs[key].decode()
create_attribute(g.id, key, [], val)
#
# Read the datasets from orbit_info
duplicate_group(f, g, 'orbit_info')
g['orbit_info/cycle_number'].dims[0].attach_scale(
g['orbit_info/crossing_time'])
g['orbit_info/lan'].dims[0].attach_scale(
g['orbit_info/crossing_time'])
g['orbit_info/orbit_number'].dims[0].attach_scale(
g['orbit_info/crossing_time'])
g['orbit_info/rgt'].dims[0].attach_scale(
g['orbit_info/crossing_time'])
g['orbit_info/sc_orient_time'].dims[0].attach_scale(
g['orbit_info/sc_orient'])
m.close()
f.close()
# Fill orbit_info for each ATL06
if ii > 0:
if os.path.isfile(infile):
f = h5py.File(infile, 'r')
for oi_dset in g['orbit_info'].values():
oi_dset.resize((oi_dset.shape[0] + 1, ))
oi_dset[-1] = f[oi_dset.name][0]
f.close()
# Capture ending dates, etc from last ATL06
if ii == len(infiles) - 1:
if os.path.isfile(infile):
f = h5py.File(infile, 'r')
for key, keyval in root_info.items():
dsname = key
if key == 'time_coverage_end':
val = f.attrs[key].decode()
create_attribute(g.id, key, [], val)
continue
if key == 'time_coverage_duration':
end_delta_time = f[
'ancillary_data/end_delta_time'][0]
val = float(end_delta_time) - float(
start_delta_time)
g.attrs[key] = val
g['ancillary_data/data_end_utc'][
...] = f['ancillary_data/data_end_utc']
g['ancillary_data/end_cycle'][
...] = f['ancillary_data/end_cycle']
g['ancillary_data/end_delta_time'][
...] = f['ancillary_data/end_delta_time']
g['ancillary_data/end_gpssow'][
...] = f['ancillary_data/end_gpssow']
g['ancillary_data/end_gpsweek'][
...] = f['ancillary_data/end_gpsweek']
g['ancillary_data/end_orbit'][
...] = f['ancillary_data/end_orbit']
g['ancillary_data/end_region'][
...] = f['ancillary_data/end_region']
g['ancillary_data/end_rgt'][
...] = f['ancillary_data/end_rgt']
g['ancillary_data/granule_end_utc'][
...] = f['ancillary_data/granule_end_utc']
g['METADATA/Extent'].attrs['rangeEndingDateTime'] = f[
'METADATA/Extent'].attrs['rangeEndingDateTime']
m.close()
f.close()
g.close()
poly_buffered_linestring(outfile)
return ()